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3 results for “safe reinforcement learning”
Boundary-Seeking Policy Gradient for Safe Reinforcement Learning
A new reinforcement learning algorithm called Boundary-Seeking Policy Gradient (BSPG) is introduced to improve safety-constrained optimization by explicitly guiding policies to the active constraint boundary—rather than settling inside the feasible region—yielding tighter constraint satisfaction and higher reward in simulation.
Aug 12, 2026
Adjustment Speed as a Safety Constraint for Nonstationary Reinforcement Learning
A new research paper introduces 'adjustment speed' as a formal safety constraint for reinforcement learning systems operating in nonstationary environments, proposing a framework that proactively restricts actions when predicted environmental adaptation demand exceeds the agent's calibrated recovery capacity.
Jul 27, 2026
SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy
A new reinforcement learning safety method called SteinGate uses Kernelized Stein Discrepancy to detect rare catastrophic tail events in policy rollouts, enabling dynamic switching between reward optimization and recovery behavior — addressing a known limitation in expected-cost-based safety constraints.
Jul 16, 2026